Overview

Dataset statistics

Number of variables54
Number of observations768
Missing cells1079
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory870.8 KiB
Average record size in memory1.1 KiB

Variable types

Categorical29
DateTime2
Numeric22
Boolean1

Dataset

DescriptionJHB_DPHRU_013 - Quality-corrected harmonized data
CreatorRP2 Clinical Data Quality Team
AuthorQuality-Checked Data
URLHEAT Research Projects

Variable descriptions

Age (at enrolment)Patient age at study enrollment
SexBiological sex
RaceRacial/ethnic group
CD4 cell count (cells/µL)CD4+ T lymphocyte count (missing codes removed)
HIV viral load (copies/mL)HIV RNA copies per mL (missing codes removed)
Antiretroviral Therapy StatusCurrent ART status
BMI (kg/m²)Body Mass Index (extreme values removed)
Waist circumference (cm)Waist circumference (corrected from mm to cm)
weight_kgBody weight in kilograms
height_mHeight in meters
Hematocrit (%)Hematocrit (zero values removed)
hemoglobin_g_dLHemoglobin concentration
White blood cell count (×10³/µL)Total WBC count
Red blood cell count (×10⁶/µL)Total RBC count
Platelet count (×10³/µL)Platelet count (missing codes removed)
MCV (MEAN CELL VOLUME)Mean corpuscular volume
mch_pgMean corpuscular hemoglobin
mchc_g_dLMean corpuscular hemoglobin concentration
RDWRed cell distribution width
Lymphocyte count (×10⁹/L)Lymphocyte absolute count (corrected labeling)
Neutrophil count (×10⁹/L)Neutrophil absolute count (corrected labeling)
Monocyte count (×10⁹/L)Monocyte absolute count (corrected labeling)
Eosinophil count (×10⁹/L)Eosinophil absolute count (corrected labeling)
Basophil count (×10⁹/L)Basophil absolute count (corrected labeling)
ALT (U/L)Alanine aminotransferase (missing codes removed)
AST (U/L)Aspartate aminotransferase
Alkaline phosphatase (U/L)Alkaline phosphatase
Total bilirubin (mg/dL)Total bilirubin
Albumin (g/dL)Serum albumin
Total protein (g/dL)Total serum protein
creatinine_umol_LSerum creatinine
creatinine clearanceEstimated creatinine clearance
Sodium (mEq/L)Serum sodium
Potassium (mEq/L)Serum potassium
fasting_glucose_mmol_LFasting blood glucose
total_cholesterol_mg_dLTotal cholesterol
hdl_cholesterol_mg_dLHDL cholesterol
ldl_cholesterol_mg_dLLDL cholesterol
Triglycerides (mg/dL)Triglycerides
systolic_bp_mmHgSystolic blood pressure
diastolic_bp_mmHgDiastolic blood pressure
heart_rate_bpmHeart rate (zero values removed)
Respiratory rate (breaths/min)Respiratory rate
Oxygen saturation (%)Oxygen saturation
body_temperature_celsiusBody temperature
climate_daily_mean_tempDaily mean temperature
climate_daily_max_tempDaily maximum temperature
climate_temp_anomalyTemperature anomaly from baseline
climate_heat_day_p90Heat day indicator (>90th percentile)
climate_heat_stress_indexHeat stress index
cd4_correction_appliedQuality flag: CD4 missing codes removed
final_comprehensive_fix_appliedQuality flag: Comprehensive corrections applied
waist_circ_unit_correction_appliedQuality flag: Waist circ unit corrected
sa_biomarker_standardsSouth African biomarker reference standards

Alerts

study_source has constant value "JHB_DPHRU_013"Constant
latitude has constant value "-26.2041"Constant
longitude has constant value "28.0473"Constant
jhb_subregion has constant value "Central_JHB"Constant
city has constant value "Johannesburg"Constant
province has constant value "Gauteng"Constant
country has constant value "South Africa"Constant
coordinate_source has constant value "JHB_DPHRU_013"Constant
coordinate_precision has constant value "high"Constant
geographic_source has constant value "harmonized_datasets"Constant
HEAT_VULNERABILITY_SCORE has constant value "0.0"Constant
HEAT_STRESS_RISK_CATEGORY has constant value "LOW"Constant
johannesburg_metro_valid has constant value "1.0"Constant
study_site_location has constant value "Central Johannesburg (DPHRU)"Constant
climate_heat_day_p95 has constant value "0.0"Constant
climate_p90_threshold has constant value "28.409"Constant
climate_p95_threshold has constant value "29.704"Constant
climate_p99_threshold has constant value "31.797"Constant
sa_biomarker_standards has constant value "1.0"Constant
cd4_correction_applied has constant value "0.0"Constant
final_comprehensive_fix_applied has constant value "1.0"Constant
total_protein_extreme_flag has constant value "0.0"Constant
dphru_053_final_corrections_applied has constant value "0.0"Constant
ezin_002_final_corrections_applied has constant value "0.0"Constant
quality_harmonization_version has constant value "2.0"Constant
BMI (kg/m²) is highly overall correlated with Waist circumference (cm) and 1 other fieldsHigh correlation
FASTING HDL is highly overall correlated with climate_heat_day_p90 and 2 other fieldsHigh correlation
FASTING LDL is highly overall correlated with climate_heat_day_p90 and 2 other fieldsHigh correlation
Waist circumference (cm) is highly overall correlated with BMI (kg/m²) and 3 other fieldsHigh correlation
climate_14d_mean_temp is highly overall correlated with climate_30d_mean_temp and 8 other fieldsHigh correlation
climate_30d_mean_temp is highly overall correlated with climate_14d_mean_temp and 8 other fieldsHigh correlation
climate_7d_max_temp is highly overall correlated with climate_30d_mean_temp and 7 other fieldsHigh correlation
climate_7d_mean_temp is highly overall correlated with climate_14d_mean_temp and 9 other fieldsHigh correlation
climate_daily_max_temp is highly overall correlated with climate_daily_mean_temp and 8 other fieldsHigh correlation
climate_daily_mean_temp is highly overall correlated with climate_14d_mean_temp and 9 other fieldsHigh correlation
climate_daily_min_temp is highly overall correlated with climate_14d_mean_temp and 9 other fieldsHigh correlation
climate_heat_day_p90 is highly overall correlated with FASTING HDL and 10 other fieldsHigh correlation
climate_heat_stress_index is highly overall correlated with climate_daily_max_temp and 4 other fieldsHigh correlation
climate_season is highly overall correlated with climate_14d_mean_temp and 12 other fieldsHigh correlation
climate_standardized_anomaly is highly overall correlated with climate_daily_max_temp and 5 other fieldsHigh correlation
climate_temp_anomaly is highly overall correlated with climate_14d_mean_temp and 10 other fieldsHigh correlation
hdl_cholesterol_mg_dL is highly overall correlated with FASTING HDL and 2 other fieldsHigh correlation
height_m is highly overall correlated with climate_heat_day_p90 and 1 other fieldsHigh correlation
ldl_cholesterol_mg_dL is highly overall correlated with FASTING LDL and 2 other fieldsHigh correlation
month is highly overall correlated with climate_7d_mean_temp and 5 other fieldsHigh correlation
season is highly overall correlated with climate_14d_mean_temp and 12 other fieldsHigh correlation
total_cholesterol_mg_dL is highly overall correlated with FASTING HDL and 4 other fieldsHigh correlation
waist_circ_unit_correction_applied is highly overall correlated with Waist circumference (cm) and 14 other fieldsHigh correlation
weight_kg is highly overall correlated with BMI (kg/m²) and 3 other fieldsHigh correlation
year is highly overall correlated with climate_14d_mean_temp and 7 other fieldsHigh correlation
climate_heat_day_p90 is highly imbalanced (98.6%)Imbalance
Age (at enrolment) has 14 (1.8%) missing valuesMissing
FASTING HDL has 58 (7.6%) missing valuesMissing
FASTING LDL has 58 (7.6%) missing valuesMissing
Waist circumference (cm) has 205 (26.7%) missing valuesMissing
fasting_glucose_mmol_L has 32 (4.2%) missing valuesMissing
total_cholesterol_mg_dL has 59 (7.7%) missing valuesMissing
hdl_cholesterol_mg_dL has 58 (7.6%) missing valuesMissing
ldl_cholesterol_mg_dL has 58 (7.6%) missing valuesMissing
weight_kg has 205 (26.7%) missing valuesMissing
height_m has 331 (43.1%) missing valuesMissing

Reproduction

Analysis started2025-11-24 22:05:46.939512
Analysis finished2025-11-24 22:06:05.227604
Duration18.29 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

study_source
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size52.5 KiB
JHB_DPHRU_013
768 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters9984
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_DPHRU_013
2nd rowJHB_DPHRU_013
3rd rowJHB_DPHRU_013
4th rowJHB_DPHRU_013
5th rowJHB_DPHRU_013

Common Values

ValueCountFrequency (%)
JHB_DPHRU_013768
100.0%

Length

2025-11-25T00:06:05.249087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:05.281927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_dphru_013768
100.0%

Most occurring characters

ValueCountFrequency (%)
H1536
15.4%
_1536
15.4%
J768
7.7%
B768
7.7%
D768
7.7%
P768
7.7%
R768
7.7%
U768
7.7%
0768
7.7%
1768
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6144
61.5%
Decimal Number2304
 
23.1%
Connector Punctuation1536
 
15.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H1536
25.0%
J768
12.5%
B768
12.5%
D768
12.5%
P768
12.5%
R768
12.5%
U768
12.5%
Decimal Number
ValueCountFrequency (%)
0768
33.3%
1768
33.3%
3768
33.3%
Connector Punctuation
ValueCountFrequency (%)
_1536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6144
61.5%
Common3840
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
H1536
25.0%
J768
12.5%
B768
12.5%
D768
12.5%
P768
12.5%
R768
12.5%
U768
12.5%
Common
ValueCountFrequency (%)
_1536
40.0%
0768
20.0%
1768
20.0%
3768
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H1536
15.4%
_1536
15.4%
J768
7.7%
B768
7.7%
D768
7.7%
P768
7.7%
R768
7.7%
U768
7.7%
0768
7.7%
1768
7.7%
Distinct232
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
Minimum2011-02-10 00:00:00
Maximum2013-06-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T00:06:05.320899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:05.371880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
2011.0
446 
2012.0
195 
2013.0
127 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4608
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011.0
2nd row2011.0
3rd row2012.0
4th row2012.0
5th row2013.0

Common Values

ValueCountFrequency (%)
2011.0446
58.1%
2012.0195
25.4%
2013.0127
 
16.5%

Length

2025-11-25T00:06:05.420021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:05.456388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2011.0446
58.1%
2012.0195
25.4%
2013.0127
 
16.5%

Most occurring characters

ValueCountFrequency (%)
01536
33.3%
11214
26.3%
2963
20.9%
.768
16.7%
3127
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3840
83.3%
Other Punctuation768
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
40.0%
11214
31.6%
2963
25.1%
3127
 
3.3%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
33.3%
11214
26.3%
2963
20.9%
.768
16.7%
3127
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
33.3%
11214
26.3%
2963
20.9%
.768
16.7%
3127
 
2.8%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.171875
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:05.492064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q38
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.6502595
Coefficient of variation (CV)0.51243688
Kurtosis-0.49910842
Mean5.171875
Median Absolute Deviation (MAD)1
Skewness0.79268233
Sum3972
Variance7.0238755
MonotonicityNot monotonic
2025-11-25T00:06:05.530459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4173
22.5%
5143
18.6%
3129
16.8%
292
12.0%
878
10.2%
1046
 
6.0%
940
 
5.2%
1134
 
4.4%
624
 
3.1%
16
 
0.8%
Other values (2)3
 
0.4%
ValueCountFrequency (%)
16
 
0.8%
292
12.0%
3129
16.8%
4173
22.5%
5143
18.6%
624
 
3.1%
72
 
0.3%
878
10.2%
940
 
5.2%
1046
 
6.0%
ValueCountFrequency (%)
121
 
0.1%
1134
 
4.4%
1046
 
6.0%
940
 
5.2%
878
10.2%
72
 
0.3%
624
 
3.1%
5143
18.6%
4173
22.5%
3129
16.8%

season
Categorical

High correlation 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
Autumn
445 
Spring
120 
Winter
104 
Summer
99 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4608
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSummer
2nd rowAutumn
3rd rowSummer
4th rowAutumn
5th rowAutumn

Common Values

ValueCountFrequency (%)
Autumn445
57.9%
Spring120
 
15.6%
Winter104
 
13.5%
Summer99
 
12.9%

Length

2025-11-25T00:06:05.570389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:05.608251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
autumn445
57.9%
spring120
 
15.6%
winter104
 
13.5%
summer99
 
12.9%

Most occurring characters

ValueCountFrequency (%)
u989
21.5%
n669
14.5%
m643
14.0%
t549
11.9%
A445
9.7%
r323
 
7.0%
i224
 
4.9%
S219
 
4.8%
e203
 
4.4%
p120
 
2.6%
Other values (2)224
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3840
83.3%
Uppercase Letter768
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u989
25.8%
n669
17.4%
m643
16.7%
t549
14.3%
r323
 
8.4%
i224
 
5.8%
e203
 
5.3%
p120
 
3.1%
g120
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A445
57.9%
S219
28.5%
W104
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Latin4608
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u989
21.5%
n669
14.5%
m643
14.0%
t549
11.9%
A445
9.7%
r323
 
7.0%
i224
 
4.9%
S219
 
4.8%
e203
 
4.4%
p120
 
2.6%
Other values (2)224
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u989
21.5%
n669
14.5%
m643
14.0%
t549
11.9%
A445
9.7%
r323
 
7.0%
i224
 
4.9%
S219
 
4.8%
e203
 
4.4%
p120
 
2.6%
Other values (2)224
 
4.9%

latitude
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
-26.2041
768 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters6144
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-26.2041
2nd row-26.2041
3rd row-26.2041
4th row-26.2041
5th row-26.2041

Common Values

ValueCountFrequency (%)
-26.2041768
100.0%

Length

2025-11-25T00:06:05.653913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:05.689716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
26.2041768
100.0%

Most occurring characters

ValueCountFrequency (%)
21536
25.0%
-768
12.5%
6768
12.5%
.768
12.5%
0768
12.5%
4768
12.5%
1768
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4608
75.0%
Dash Punctuation768
 
12.5%
Other Punctuation768
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21536
33.3%
6768
16.7%
0768
16.7%
4768
16.7%
1768
16.7%
Dash Punctuation
ValueCountFrequency (%)
-768
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6144
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
21536
25.0%
-768
12.5%
6768
12.5%
.768
12.5%
0768
12.5%
4768
12.5%
1768
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21536
25.0%
-768
12.5%
6768
12.5%
.768
12.5%
0768
12.5%
4768
12.5%
1768
12.5%

longitude
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.0 KiB
28.0473
768 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters5376
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.0473
2nd row28.0473
3rd row28.0473
4th row28.0473
5th row28.0473

Common Values

ValueCountFrequency (%)
28.0473768
100.0%

Length

2025-11-25T00:06:05.724933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:05.758700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
28.0473768
100.0%

Most occurring characters

ValueCountFrequency (%)
2768
14.3%
8768
14.3%
.768
14.3%
0768
14.3%
4768
14.3%
7768
14.3%
3768
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4608
85.7%
Other Punctuation768
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2768
16.7%
8768
16.7%
0768
16.7%
4768
16.7%
7768
16.7%
3768
16.7%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2768
14.3%
8768
14.3%
.768
14.3%
0768
14.3%
4768
14.3%
7768
14.3%
3768
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2768
14.3%
8768
14.3%
.768
14.3%
0768
14.3%
4768
14.3%
7768
14.3%
3768
14.3%

jhb_subregion
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
Central_JHB
768 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters8448
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral_JHB
2nd rowCentral_JHB
3rd rowCentral_JHB
4th rowCentral_JHB
5th rowCentral_JHB

Common Values

ValueCountFrequency (%)
Central_JHB768
100.0%

Length

2025-11-25T00:06:05.792190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:05.825147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
central_jhb768
100.0%

Most occurring characters

ValueCountFrequency (%)
C768
9.1%
e768
9.1%
n768
9.1%
t768
9.1%
r768
9.1%
a768
9.1%
l768
9.1%
_768
9.1%
J768
9.1%
H768
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4608
54.5%
Uppercase Letter3072
36.4%
Connector Punctuation768
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e768
16.7%
n768
16.7%
t768
16.7%
r768
16.7%
a768
16.7%
l768
16.7%
Uppercase Letter
ValueCountFrequency (%)
C768
25.0%
J768
25.0%
H768
25.0%
B768
25.0%
Connector Punctuation
ValueCountFrequency (%)
_768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7680
90.9%
Common768
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C768
10.0%
e768
10.0%
n768
10.0%
t768
10.0%
r768
10.0%
a768
10.0%
l768
10.0%
J768
10.0%
H768
10.0%
B768
10.0%
Common
ValueCountFrequency (%)
_768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C768
9.1%
e768
9.1%
n768
9.1%
t768
9.1%
r768
9.1%
a768
9.1%
l768
9.1%
_768
9.1%
J768
9.1%
H768
9.1%

city
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.8 KiB
Johannesburg
768 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters9216
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohannesburg
2nd rowJohannesburg
3rd rowJohannesburg
4th rowJohannesburg
5th rowJohannesburg

Common Values

ValueCountFrequency (%)
Johannesburg768
100.0%

Length

2025-11-25T00:06:05.858990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:05.891795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
johannesburg768
100.0%

Most occurring characters

ValueCountFrequency (%)
n1536
16.7%
J768
8.3%
o768
8.3%
h768
8.3%
a768
8.3%
e768
8.3%
s768
8.3%
b768
8.3%
u768
8.3%
r768
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8448
91.7%
Uppercase Letter768
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1536
18.2%
o768
9.1%
h768
9.1%
a768
9.1%
e768
9.1%
s768
9.1%
b768
9.1%
u768
9.1%
r768
9.1%
g768
9.1%
Uppercase Letter
ValueCountFrequency (%)
J768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9216
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1536
16.7%
J768
8.3%
o768
8.3%
h768
8.3%
a768
8.3%
e768
8.3%
s768
8.3%
b768
8.3%
u768
8.3%
r768
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1536
16.7%
J768
8.3%
o768
8.3%
h768
8.3%
a768
8.3%
e768
8.3%
s768
8.3%
b768
8.3%
u768
8.3%
r768
8.3%

province
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.0 KiB
Gauteng
768 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters5376
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGauteng
2nd rowGauteng
3rd rowGauteng
4th rowGauteng
5th rowGauteng

Common Values

ValueCountFrequency (%)
Gauteng768
100.0%

Length

2025-11-25T00:06:05.927102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:05.960495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gauteng768
100.0%

Most occurring characters

ValueCountFrequency (%)
G768
14.3%
a768
14.3%
u768
14.3%
t768
14.3%
e768
14.3%
n768
14.3%
g768
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4608
85.7%
Uppercase Letter768
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a768
16.7%
u768
16.7%
t768
16.7%
e768
16.7%
n768
16.7%
g768
16.7%
Uppercase Letter
ValueCountFrequency (%)
G768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5376
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G768
14.3%
a768
14.3%
u768
14.3%
t768
14.3%
e768
14.3%
n768
14.3%
g768
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G768
14.3%
a768
14.3%
u768
14.3%
t768
14.3%
e768
14.3%
n768
14.3%
g768
14.3%

country
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.8 KiB
South Africa
768 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters9216
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Africa
2nd rowSouth Africa
3rd rowSouth Africa
4th rowSouth Africa
5th rowSouth Africa

Common Values

ValueCountFrequency (%)
South Africa768
100.0%

Length

2025-11-25T00:06:05.998868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:06.031312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
south768
50.0%
africa768
50.0%

Most occurring characters

ValueCountFrequency (%)
S768
8.3%
o768
8.3%
u768
8.3%
t768
8.3%
h768
8.3%
768
8.3%
A768
8.3%
f768
8.3%
r768
8.3%
i768
8.3%
Other values (2)1536
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6912
75.0%
Uppercase Letter1536
 
16.7%
Space Separator768
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o768
11.1%
u768
11.1%
t768
11.1%
h768
11.1%
f768
11.1%
r768
11.1%
i768
11.1%
c768
11.1%
a768
11.1%
Uppercase Letter
ValueCountFrequency (%)
S768
50.0%
A768
50.0%
Space Separator
ValueCountFrequency (%)
768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8448
91.7%
Common768
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S768
9.1%
o768
9.1%
u768
9.1%
t768
9.1%
h768
9.1%
A768
9.1%
f768
9.1%
r768
9.1%
i768
9.1%
c768
9.1%
Common
ValueCountFrequency (%)
768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S768
8.3%
o768
8.3%
u768
8.3%
t768
8.3%
h768
8.3%
768
8.3%
A768
8.3%
f768
8.3%
r768
8.3%
i768
8.3%
Other values (2)1536
16.7%

Age (at enrolment)
Real number (ℝ)

Missing 

Patient age at study enrollment

Distinct183
Distinct (%)24.3%
Missing14
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean33.533554
Minimum18.1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:06.069621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.1
5-th percentile22
Q127.85
median33.95
Q339
95-th percentile46
Maximum51
Range32.9
Interquartile range (IQR)11.15

Descriptive statistics

Standard deviation7.3527855
Coefficient of variation (CV)0.21926651
Kurtosis-0.80768914
Mean33.533554
Median Absolute Deviation (MAD)5.95
Skewness0.055500259
Sum25284.3
Variance54.063454
MonotonicityNot monotonic
2025-11-25T00:06:06.117785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4032
 
4.2%
3431
 
4.0%
3931
 
4.0%
3729
 
3.8%
3027
 
3.5%
3125
 
3.3%
3823
 
3.0%
4122
 
2.9%
2621
 
2.7%
3521
 
2.7%
Other values (173)492
64.1%
ValueCountFrequency (%)
18.11
 
0.1%
18.81
 
0.1%
193
 
0.4%
19.31
 
0.1%
19.42
 
0.3%
19.51
 
0.1%
19.61
 
0.1%
209
1.2%
20.11
 
0.1%
20.61
 
0.1%
ValueCountFrequency (%)
511
 
0.1%
503
 
0.4%
49.11
 
0.1%
495
0.7%
488
1.0%
47.91
 
0.1%
47.21
 
0.1%
4710
1.3%
46.61
 
0.1%
46.41
 
0.1%

date
Date

Distinct232
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
Minimum2011-02-10 00:00:00
Maximum2013-06-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T00:06:06.165917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:06.217856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

FASTING HDL
Real number (ℝ)

High correlation  Missing 

Distinct174
Distinct (%)24.5%
Missing58
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.1211127
Minimum0.28
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:06.267211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.51
Q10.83
median1.07
Q31.37
95-th percentile1.8855
Maximum3.7
Range3.42
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation0.44352229
Coefficient of variation (CV)0.39560902
Kurtosis4.4712255
Mean1.1211127
Median Absolute Deviation (MAD)0.26
Skewness1.2913394
Sum795.99
Variance0.19671202
MonotonicityNot monotonic
2025-11-25T00:06:06.314257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.813
 
1.7%
1.0413
 
1.7%
0.8513
 
1.7%
0.9313
 
1.7%
1.113
 
1.7%
1.1811
 
1.4%
0.9511
 
1.4%
110
 
1.3%
0.8410
 
1.3%
0.879
 
1.2%
Other values (164)594
77.3%
(Missing)58
 
7.6%
ValueCountFrequency (%)
0.281
0.1%
0.321
0.1%
0.332
0.3%
0.342
0.3%
0.351
0.1%
0.362
0.3%
0.372
0.3%
0.391
0.1%
0.42
0.3%
0.412
0.3%
ValueCountFrequency (%)
3.73
0.4%
2.81
 
0.1%
2.532
0.3%
2.491
 
0.1%
2.441
 
0.1%
2.311
 
0.1%
2.31
 
0.1%
2.291
 
0.1%
2.242
0.3%
2.231
 
0.1%

FASTING LDL
Real number (ℝ)

High correlation  Missing 

Distinct261
Distinct (%)36.8%
Missing58
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.6717042
Minimum0
Maximum6.04
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:06.360633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6745
Q11.11
median1.535
Q32.07
95-th percentile3.18
Maximum6.04
Range6.04
Interquartile range (IQR)0.96

Descriptive statistics

Standard deviation0.77008108
Coefficient of variation (CV)0.4606563
Kurtosis1.8978142
Mean1.6717042
Median Absolute Deviation (MAD)0.475
Skewness1.0866871
Sum1186.91
Variance0.59302488
MonotonicityNot monotonic
2025-11-25T00:06:06.409876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.019
 
1.2%
1.129
 
1.2%
1.329
 
1.2%
1.378
 
1.0%
1.298
 
1.0%
1.187
 
0.9%
2.067
 
0.9%
1.947
 
0.9%
1.267
 
0.9%
1.767
 
0.9%
Other values (251)632
82.3%
(Missing)58
 
7.6%
ValueCountFrequency (%)
01
 
0.1%
0.331
 
0.1%
0.391
 
0.1%
0.422
 
0.3%
0.451
 
0.1%
0.461
 
0.1%
0.471
 
0.1%
0.53
0.4%
0.555
0.7%
0.564
0.5%
ValueCountFrequency (%)
6.041
0.1%
4.411
0.1%
4.281
0.1%
4.252
0.3%
4.191
0.1%
4.131
0.1%
3.971
0.1%
3.941
0.1%
3.891
0.1%
3.871
0.1%

coordinate_source
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size52.5 KiB
JHB_DPHRU_013
768 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters9984
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_DPHRU_013
2nd rowJHB_DPHRU_013
3rd rowJHB_DPHRU_013
4th rowJHB_DPHRU_013
5th rowJHB_DPHRU_013

Common Values

ValueCountFrequency (%)
JHB_DPHRU_013768
100.0%

Length

2025-11-25T00:06:06.452791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:06.573612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_dphru_013768
100.0%

Most occurring characters

ValueCountFrequency (%)
H1536
15.4%
_1536
15.4%
J768
7.7%
B768
7.7%
D768
7.7%
P768
7.7%
R768
7.7%
U768
7.7%
0768
7.7%
1768
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6144
61.5%
Decimal Number2304
 
23.1%
Connector Punctuation1536
 
15.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H1536
25.0%
J768
12.5%
B768
12.5%
D768
12.5%
P768
12.5%
R768
12.5%
U768
12.5%
Decimal Number
ValueCountFrequency (%)
0768
33.3%
1768
33.3%
3768
33.3%
Connector Punctuation
ValueCountFrequency (%)
_1536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6144
61.5%
Common3840
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
H1536
25.0%
J768
12.5%
B768
12.5%
D768
12.5%
P768
12.5%
R768
12.5%
U768
12.5%
Common
ValueCountFrequency (%)
_1536
40.0%
0768
20.0%
1768
20.0%
3768
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H1536
15.4%
_1536
15.4%
J768
7.7%
B768
7.7%
D768
7.7%
P768
7.7%
R768
7.7%
U768
7.7%
0768
7.7%
1768
7.7%

coordinate_precision
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.8 KiB
high
768 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3072
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhigh
2nd rowhigh
3rd rowhigh
4th rowhigh
5th rowhigh

Common Values

ValueCountFrequency (%)
high768
100.0%

Length

2025-11-25T00:06:06.610769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:06.646549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
high768
100.0%

Most occurring characters

ValueCountFrequency (%)
h1536
50.0%
i768
25.0%
g768
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3072
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h1536
50.0%
i768
25.0%
g768
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3072
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
h1536
50.0%
i768
25.0%
g768
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h1536
50.0%
i768
25.0%
g768
25.0%

geographic_source
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.0 KiB
harmonized_datasets
768 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters14592
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowharmonized_datasets
2nd rowharmonized_datasets
3rd rowharmonized_datasets
4th rowharmonized_datasets
5th rowharmonized_datasets

Common Values

ValueCountFrequency (%)
harmonized_datasets768
100.0%

Length

2025-11-25T00:06:06.683946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:06.719133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
harmonized_datasets768
100.0%

Most occurring characters

ValueCountFrequency (%)
a2304
15.8%
e1536
10.5%
d1536
10.5%
t1536
10.5%
s1536
10.5%
h768
 
5.3%
r768
 
5.3%
m768
 
5.3%
o768
 
5.3%
n768
 
5.3%
Other values (3)2304
15.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13824
94.7%
Connector Punctuation768
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2304
16.7%
e1536
11.1%
d1536
11.1%
t1536
11.1%
s1536
11.1%
h768
 
5.6%
r768
 
5.6%
m768
 
5.6%
o768
 
5.6%
n768
 
5.6%
Other values (2)1536
11.1%
Connector Punctuation
ValueCountFrequency (%)
_768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13824
94.7%
Common768
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2304
16.7%
e1536
11.1%
d1536
11.1%
t1536
11.1%
s1536
11.1%
h768
 
5.6%
r768
 
5.6%
m768
 
5.6%
o768
 
5.6%
n768
 
5.6%
Other values (2)1536
11.1%
Common
ValueCountFrequency (%)
_768
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2304
15.8%
e1536
10.5%
d1536
10.5%
t1536
10.5%
s1536
10.5%
h768
 
5.3%
r768
 
5.3%
m768
 
5.3%
o768
 
5.3%
n768
 
5.3%
Other values (3)2304
15.8%

HEAT_VULNERABILITY_SCORE
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0768
100.0%

Length

2025-11-25T00:06:06.756979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:06.790222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

HEAT_STRESS_RISK_CATEGORY
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
LOW
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLOW
2nd rowLOW
3rd rowLOW
4th rowLOW
5th rowLOW

Common Values

ValueCountFrequency (%)
LOW768
100.0%

Length

2025-11-25T00:06:06.827164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:06.862847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
low768
100.0%

Most occurring characters

ValueCountFrequency (%)
L768
33.3%
O768
33.3%
W768
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2304
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L768
33.3%
O768
33.3%
W768
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2304
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L768
33.3%
O768
33.3%
W768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L768
33.3%
O768
33.3%
W768
33.3%

johannesburg_metro_valid
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
1.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0768
100.0%

Length

2025-11-25T00:06:06.899684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:06.932367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1768
50.0%
0768
50.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

study_site_location
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.8 KiB
Central Johannesburg (DPHRU)
768 

Length

Max length28
Median length28
Mean length28
Min length28

Characters and Unicode

Total characters21504
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral Johannesburg (DPHRU)
2nd rowCentral Johannesburg (DPHRU)
3rd rowCentral Johannesburg (DPHRU)
4th rowCentral Johannesburg (DPHRU)
5th rowCentral Johannesburg (DPHRU)

Common Values

ValueCountFrequency (%)
Central Johannesburg (DPHRU)768
100.0%

Length

2025-11-25T00:06:06.968097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:07.003744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
central768
33.3%
johannesburg768
33.3%
dphru768
33.3%

Most occurring characters

ValueCountFrequency (%)
n2304
 
10.7%
r1536
 
7.1%
a1536
 
7.1%
1536
 
7.1%
e1536
 
7.1%
C768
 
3.6%
g768
 
3.6%
U768
 
3.6%
R768
 
3.6%
H768
 
3.6%
Other values (12)9216
42.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13056
60.7%
Uppercase Letter5376
25.0%
Space Separator1536
 
7.1%
Open Punctuation768
 
3.6%
Close Punctuation768
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n2304
17.6%
r1536
11.8%
a1536
11.8%
e1536
11.8%
g768
 
5.9%
s768
 
5.9%
u768
 
5.9%
b768
 
5.9%
h768
 
5.9%
o768
 
5.9%
Other values (2)1536
11.8%
Uppercase Letter
ValueCountFrequency (%)
C768
14.3%
U768
14.3%
R768
14.3%
H768
14.3%
P768
14.3%
D768
14.3%
J768
14.3%
Space Separator
ValueCountFrequency (%)
1536
100.0%
Open Punctuation
ValueCountFrequency (%)
(768
100.0%
Close Punctuation
ValueCountFrequency (%)
)768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18432
85.7%
Common3072
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n2304
 
12.5%
r1536
 
8.3%
a1536
 
8.3%
e1536
 
8.3%
C768
 
4.2%
g768
 
4.2%
U768
 
4.2%
R768
 
4.2%
H768
 
4.2%
P768
 
4.2%
Other values (9)6912
37.5%
Common
ValueCountFrequency (%)
1536
50.0%
(768
25.0%
)768
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII21504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n2304
 
10.7%
r1536
 
7.1%
a1536
 
7.1%
1536
 
7.1%
e1536
 
7.1%
C768
 
3.6%
g768
 
3.6%
U768
 
3.6%
R768
 
3.6%
H768
 
3.6%
Other values (12)9216
42.9%

BMI (kg/m²)
Real number (ℝ)

High correlation 

Body Mass Index (extreme values removed)

Distinct248
Distinct (%)32.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean27.852803
Minimum15.1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:07.042046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15.1
5-th percentile19.13
Q123
median26.7
Q331.5
95-th percentile40.54
Maximum57
Range41.9
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation6.6900116
Coefficient of variation (CV)0.24019168
Kurtosis1.6551874
Mean27.852803
Median Absolute Deviation (MAD)4.1
Skewness1.0682353
Sum21363.1
Variance44.756255
MonotonicityNot monotonic
2025-11-25T00:06:07.086762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.311
 
1.4%
2510
 
1.3%
21.810
 
1.3%
26.79
 
1.2%
27.48
 
1.0%
25.88
 
1.0%
32.38
 
1.0%
21.58
 
1.0%
22.98
 
1.0%
28.97
 
0.9%
Other values (238)680
88.5%
ValueCountFrequency (%)
15.11
0.1%
15.31
0.1%
161
0.1%
16.11
0.1%
16.61
0.1%
16.81
0.1%
16.91
0.1%
17.11
0.1%
17.21
0.1%
17.31
0.1%
ValueCountFrequency (%)
571
 
0.1%
56.11
 
0.1%
54.91
 
0.1%
54.31
 
0.1%
50.71
 
0.1%
50.41
 
0.1%
50.11
 
0.1%
49.83
0.4%
491
 
0.1%
46.42
0.3%

Waist circumference (cm)
Real number (ℝ)

High correlation  Missing 

Waist circumference (corrected from mm to cm)

Distinct115
Distinct (%)20.4%
Missing205
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean89.362345
Minimum2.9
Maximum915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:07.132050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile67.55
Q178
median86.5
Q396.5
95-th percentile115.25
Maximum915
Range912.1
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation38.256862
Coefficient of variation (CV)0.42810942
Kurtosis387.32681
Mean89.362345
Median Absolute Deviation (MAD)8.5
Skewness17.935657
Sum50311
Variance1463.5875
MonotonicityNot monotonic
2025-11-25T00:06:07.179166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8722
 
2.9%
8519
 
2.5%
8119
 
2.5%
7818
 
2.3%
8918
 
2.3%
8617
 
2.2%
7916
 
2.1%
7416
 
2.1%
7615
 
2.0%
7713
 
1.7%
Other values (105)390
50.8%
(Missing)205
26.7%
ValueCountFrequency (%)
2.91
 
0.1%
8.11
 
0.1%
10.81
 
0.1%
591
 
0.1%
612
0.3%
621
 
0.1%
634
0.5%
641
 
0.1%
652
0.3%
664
0.5%
ValueCountFrequency (%)
9151
0.1%
1511
0.1%
1451
0.1%
143.51
0.1%
1401
0.1%
1331
0.1%
1311
0.1%
1301
0.1%
129.51
0.1%
1282
0.3%

climate_daily_mean_temp
Real number (ℝ)

High correlation 

Daily mean temperature

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.209534
Minimum8.507
Maximum21.131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:07.219321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.507
5-th percentile13.316
Q114.603
median16.425
Q317.799
95-th percentile20.357
Maximum21.131
Range12.624
Interquartile range (IQR)3.196

Descriptive statistics

Standard deviation2.3493444
Coefficient of variation (CV)0.14493596
Kurtosis0.37172559
Mean16.209534
Median Absolute Deviation (MAD)1.74
Skewness-0.23360933
Sum12448.922
Variance5.519419
MonotonicityNot monotonic
2025-11-25T00:06:07.260858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
16.42578
10.2%
17.03971
 
9.2%
17.79970
 
9.1%
15.6757
 
7.4%
14.20956
 
7.3%
14.60354
 
7.0%
13.31653
 
6.9%
14.68549
 
6.4%
20.35749
 
6.4%
17.54146
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
8.5072
 
0.3%
9.61621
 
2.7%
13.31653
6.9%
13.6563
 
0.4%
14.20956
7.3%
14.5534
4.4%
14.60354
7.0%
14.68549
6.4%
14.86240
5.2%
15.6757
7.4%
ValueCountFrequency (%)
21.1311
 
0.1%
20.4656
 
0.8%
20.35749
6.4%
20.2931
 
0.1%
19.59943
5.6%
19.08434
4.4%
17.79970
9.1%
17.54146
6.0%
17.03971
9.2%
16.42578
10.2%

climate_daily_max_temp
Real number (ℝ)

High correlation 

Daily maximum temperature

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.380574
Minimum14.624
Maximum28.861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:07.297879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14.624
5-th percentile18.896
Q120.108
median20.768
Q325.325
95-th percentile25.931
Maximum28.861
Range14.237
Interquartile range (IQR)5.217

Descriptive statistics

Standard deviation2.9122833
Coefficient of variation (CV)0.13012549
Kurtosis-1.5446717
Mean22.380574
Median Absolute Deviation (MAD)2.695
Skewness0.0051205477
Sum17188.281
Variance8.4813938
MonotonicityNot monotonic
2025-11-25T00:06:07.337310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
25.93178
10.2%
20.32471
 
9.2%
25.870
 
9.1%
19.12557
 
7.4%
19.27756
 
7.3%
20.58954
 
7.0%
20.76853
 
6.9%
18.89649
 
6.4%
24.31949
 
6.4%
25.00546
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
14.6242
 
0.3%
17.34421
 
2.7%
18.89649
6.4%
19.12557
7.4%
19.27756
7.3%
20.10834
4.4%
20.32471
9.2%
20.58954
7.0%
20.76853
6.9%
21.4743
 
0.4%
ValueCountFrequency (%)
28.8611
 
0.1%
26.7691
 
0.1%
26.13634
4.4%
25.93178
10.2%
25.870
9.1%
25.5726
 
0.8%
25.32543
5.6%
25.00546
6.0%
24.31949
6.4%
23.46340
5.2%

climate_daily_min_temp
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.377281
Minimum3.333
Maximum17.507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:07.375390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.333
5-th percentile6.306
Q17.463
median9.869
Q312.877
95-th percentile17.507
Maximum17.507
Range14.174
Interquartile range (IQR)5.414

Descriptive statistics

Standard deviation3.3901858
Coefficient of variation (CV)0.32669307
Kurtosis-0.53633939
Mean10.377281
Median Absolute Deviation (MAD)3.008
Skewness0.26308779
Sum7969.752
Variance11.49336
MonotonicityNot monotonic
2025-11-25T00:06:07.414432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6.30678
10.2%
14.29171
 
9.2%
10.49370
 
9.1%
12.87757
 
7.4%
8.7656
 
7.3%
9.00454
 
7.0%
6.61653
 
6.9%
11.18749
 
6.4%
17.50749
 
6.4%
9.86946
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
3.33321
 
2.7%
3.9932
 
0.3%
6.0343
 
0.4%
6.30678
10.2%
6.61653
6.9%
7.46340
5.2%
8.03534
4.4%
8.7656
7.3%
9.00454
7.0%
9.86946
6.0%
ValueCountFrequency (%)
17.50749
6.4%
16.5761
 
0.1%
14.29171
9.2%
14.05743
5.6%
13.9681
 
0.1%
13.4546
 
0.8%
12.87757
7.4%
12.46534
4.4%
11.18749
6.4%
10.49370
9.1%

climate_7d_mean_temp
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.074818
Minimum6.912
Maximum20.253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:07.454317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.912
5-th percentile12.665
Q114.198
median16.453
Q318.033
95-th percentile20.253
Maximum20.253
Range13.341
Interquartile range (IQR)3.835

Descriptive statistics

Standard deviation2.5447401
Coefficient of variation (CV)0.158306
Kurtosis0.40543248
Mean16.074818
Median Absolute Deviation (MAD)1.604
Skewness-0.46441855
Sum12345.46
Variance6.4757021
MonotonicityNot monotonic
2025-11-25T00:06:07.497427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
14.19878
10.2%
18.08171
 
9.2%
16.47170
 
9.1%
18.03357
 
7.4%
13.24356
 
7.3%
15.06454
 
7.0%
12.66553
 
6.9%
15.63349
 
6.4%
19.98249
 
6.4%
16.45346
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
6.9122
 
0.3%
8.99321
 
2.7%
10.7933
 
0.4%
12.66553
6.9%
13.24356
7.3%
14.19878
10.2%
15.06454
7.0%
15.0834
4.4%
15.63349
6.4%
16.45346
6.0%
ValueCountFrequency (%)
20.25343
5.6%
19.98249
6.4%
19.8651
 
0.1%
19.6316
 
0.8%
18.7961
 
0.1%
18.08171
9.2%
18.03357
7.4%
17.69534
4.4%
16.7140
5.2%
16.47170
9.1%

climate_7d_max_temp
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.610668
Minimum17.442
Maximum29.909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:07.536825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17.442
5-th percentile20.428
Q123.496
median25.893
Q326.761
95-th percentile27.9
Maximum29.909
Range12.467
Interquartile range (IQR)3.265

Descriptive statistics

Standard deviation2.7109412
Coefficient of variation (CV)0.11015309
Kurtosis-0.98377234
Mean24.610668
Median Absolute Deviation (MAD)2.007
Skewness-0.56315645
Sum18900.993
Variance7.3492024
MonotonicityNot monotonic
2025-11-25T00:06:07.579840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
26.29278
10.2%
23.49671
 
9.2%
26.76170
 
9.1%
27.957
 
7.4%
20.42856
 
7.3%
21.26454
 
7.0%
20.76853
 
6.9%
23.49849
 
6.4%
25.89349
 
6.4%
27.69946
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
17.4422
 
0.3%
18.69921
 
2.7%
20.42856
7.3%
20.76853
6.9%
21.26454
7.0%
21.9773
 
0.4%
23.49671
9.2%
23.49849
6.4%
23.70534
4.4%
25.89349
6.4%
ValueCountFrequency (%)
29.9091
 
0.1%
28.6961
 
0.1%
27.957
7.4%
27.69946
6.0%
27.10543
5.6%
26.76170
9.1%
26.5140
5.2%
26.29278
10.2%
26.20434
4.4%
26.0456
 
0.8%

climate_14d_mean_temp
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.391837
Minimum7.302
Maximum20.679
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:07.618954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.302
5-th percentile12.57
Q114.8
median16.561
Q319.009
95-th percentile20.679
Maximum20.679
Range13.377
Interquartile range (IQR)4.209

Descriptive statistics

Standard deviation2.7566005
Coefficient of variation (CV)0.1681691
Kurtosis0.093105997
Mean16.391837
Median Absolute Deviation (MAD)2.184
Skewness-0.53703163
Sum12588.931
Variance7.5988464
MonotonicityNot monotonic
2025-11-25T00:06:07.661430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
14.878
10.2%
19.00971
 
9.2%
16.05770
 
9.1%
18.74557
 
7.4%
12.69956
 
7.3%
15.48354
 
7.0%
12.5753
 
6.9%
16.63749
 
6.4%
20.67949
 
6.4%
16.86346
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
7.3022
 
0.3%
8.82621
 
2.7%
11.5323
 
0.4%
12.5753
6.9%
12.69956
7.3%
14.878
10.2%
15.22634
4.4%
15.48354
7.0%
16.05770
9.1%
16.56140
5.2%
ValueCountFrequency (%)
20.67949
6.4%
20.2621
 
0.1%
19.62934
4.4%
19.47543
5.6%
19.3346
 
0.8%
19.0721
 
0.1%
19.00971
9.2%
18.74557
7.4%
16.86346
6.0%
16.63749
6.4%

climate_30d_mean_temp
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.449453
Minimum7.313
Maximum20.948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:07.701824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.313
5-th percentile12.856
Q113.947
median15.844
Q319.139
95-th percentile20.6
Maximum20.948
Range13.635
Interquartile range (IQR)5.192

Descriptive statistics

Standard deviation2.8236221
Coefficient of variation (CV)0.17165447
Kurtosis-0.51241703
Mean16.449453
Median Absolute Deviation (MAD)2.843
Skewness-0.25639706
Sum12633.18
Variance7.9728417
MonotonicityNot monotonic
2025-11-25T00:06:07.744633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
13.94778
10.2%
19.13971
 
9.2%
15.77570
 
9.1%
19.21757
 
7.4%
13.00156
 
7.3%
15.73454
 
7.0%
12.85653
 
6.9%
16.73249
 
6.4%
20.649
 
6.4%
17.57246
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
7.3132
 
0.3%
9.58421
 
2.7%
11.6353
 
0.4%
12.85653
6.9%
13.00156
7.3%
13.94778
10.2%
15.20834
4.4%
15.73454
7.0%
15.77570
9.1%
15.84440
5.2%
ValueCountFrequency (%)
20.94834
4.4%
20.649
6.4%
20.2631
 
0.1%
20.1756
 
0.8%
19.4761
 
0.1%
19.21757
7.4%
19.13971
9.2%
18.96643
5.6%
17.57246
6.0%
16.73249
6.4%

climate_temp_anomaly
Real number (ℝ)

High correlation 

Temperature anomaly from baseline

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.931276
Minimum-0.093
Maximum11.984
Zeros0
Zeros (%)0.0%
Negative57
Negative (%)7.4%
Memory size12.0 KiB
2025-11-25T00:06:07.784974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.093
5-th percentile-0.093
Q13.719
median6.276
Q37.913
95-th percentile11.984
Maximum11.984
Range12.077
Interquartile range (IQR)4.194

Descriptive statistics

Standard deviation3.4869388
Coefficient of variation (CV)0.58789016
Kurtosis-0.79783053
Mean5.931276
Median Absolute Deviation (MAD)1.637
Skewness-0.0032101025
Sum4555.22
Variance12.158742
MonotonicityNot monotonic
2025-11-25T00:06:07.827765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
11.98478
10.2%
1.18571
 
9.2%
10.02570
 
9.1%
-0.09357
 
7.4%
6.27656
 
7.3%
4.85554
 
7.0%
7.91353
 
6.9%
2.16449
 
6.4%
3.71949
 
6.4%
7.43446
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
-0.09357
7.4%
1.18571
9.2%
2.16449
6.4%
3.71949
6.4%
4.85554
7.0%
4.934
4.4%
5.18934
4.4%
5.3976
 
0.8%
6.27656
7.3%
6.3643
5.6%
ValueCountFrequency (%)
11.98478
10.2%
10.02570
9.1%
9.8393
 
0.4%
9.3861
 
0.1%
7.91353
6.9%
7.7621
 
2.7%
7.61940
5.2%
7.43446
6.0%
7.3112
 
0.3%
6.5051
 
0.1%

climate_standardized_anomaly
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0078164063
Minimum-1.401
Maximum1.604
Zeros0
Zeros (%)0.0%
Negative417
Negative (%)54.3%
Memory size12.0 KiB
2025-11-25T00:06:07.864790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.401
5-th percentile-1.401
Q1-0.449
median-0.069
Q30.571
95-th percentile1.09
Maximum1.604
Range3.005
Interquartile range (IQR)1.02

Descriptive statistics

Standard deviation0.75070013
Coefficient of variation (CV)96.041596
Kurtosis-0.92389359
Mean0.0078164063
Median Absolute Deviation (MAD)0.633
Skewness-0.015229641
Sum6.003
Variance0.56355069
MonotonicityNot monotonic
2025-11-25T00:06:07.904882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1.0978
10.2%
-0.70271
 
9.2%
1.07470
 
9.1%
-1.40157
 
7.4%
0.57156
 
7.3%
-0.44954
 
7.0%
0.1953
 
6.9%
-0.4149
 
6.4%
0.29849
 
6.4%
-0.44546
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
-1.40157
7.4%
-0.86840
5.2%
-0.70271
9.2%
-0.622
 
0.3%
-0.44954
7.0%
-0.44546
6.0%
-0.4149
6.4%
-0.19321
 
2.7%
-0.13743
5.6%
-0.06934
4.4%
ValueCountFrequency (%)
1.6043
 
0.4%
1.0978
10.2%
1.07470
9.1%
0.9591
 
0.1%
0.71634
4.4%
0.57156
7.3%
0.4931
 
0.1%
0.29849
6.4%
0.2176
 
0.8%
0.1953
6.9%

climate_heat_day_p90
Categorical

High correlation  Imbalance 

Heat day indicator (>90th percentile)

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
767 
0.571
 
1

Length

Max length5
Median length3
Mean length3.0026042
Min length3

Characters and Unicode

Total characters2306
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0767
99.9%
0.5711
 
0.1%

Length

2025-11-25T00:06:07.950758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:07.988944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0767
99.9%
0.5711
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01535
66.6%
.768
33.3%
51
 
< 0.1%
71
 
< 0.1%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1538
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01535
99.8%
51
 
0.1%
71
 
0.1%
11
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2306
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01535
66.6%
.768
33.3%
51
 
< 0.1%
71
 
< 0.1%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01535
66.6%
.768
33.3%
51
 
< 0.1%
71
 
< 0.1%
11
 
< 0.1%

climate_heat_day_p95
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0768
100.0%

Length

2025-11-25T00:06:08.026193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:08.060351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

climate_heat_stress_index
Real number (ℝ)

High correlation 

Heat stress index

Distinct20
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.523178
Minimum2.218
Maximum24.693
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:08.093978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.218
5-th percentile14.373
Q115.36
median16.691
Q320.175
95-th percentile21.262
Maximum24.693
Range22.475
Interquartile range (IQR)4.815

Descriptive statistics

Standard deviation2.7052804
Coefficient of variation (CV)0.15438298
Kurtosis1.0512418
Mean17.523178
Median Absolute Deviation (MAD)1.757
Skewness-0.1762757
Sum13457.801
Variance7.3185422
MonotonicityNot monotonic
2025-11-25T00:06:08.131342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
21.05778
10.2%
16.47671
 
9.2%
19.95870
 
9.1%
15.08857
 
7.4%
14.93456
 
7.3%
16.76554
 
7.0%
15.72153
 
6.9%
14.37349
 
6.4%
20.51849
 
6.4%
21.26246
 
6.0%
Other values (10)185
24.1%
ValueCountFrequency (%)
2.2182
 
0.3%
13.35121
 
2.7%
13.4283
 
0.4%
14.37349
6.4%
14.93456
7.3%
15.08857
7.4%
15.3634
4.4%
15.72153
6.9%
16.44234
4.4%
16.47671
9.2%
ValueCountFrequency (%)
24.6931
 
0.1%
22.8676
 
0.8%
22.5261
 
0.1%
21.26246
6.0%
21.05778
10.2%
20.51849
6.4%
20.17543
5.6%
19.95870
9.1%
16.76554
7.0%
16.69140
5.2%

climate_p90_threshold
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
28.409
768 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4608
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.409
2nd row28.409
3rd row28.409
4th row28.409
5th row28.409

Common Values

ValueCountFrequency (%)
28.409768
100.0%

Length

2025-11-25T00:06:08.172887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:08.206686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
28.409768
100.0%

Most occurring characters

ValueCountFrequency (%)
2768
16.7%
8768
16.7%
.768
16.7%
4768
16.7%
0768
16.7%
9768
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3840
83.3%
Other Punctuation768
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2768
20.0%
8768
20.0%
4768
20.0%
0768
20.0%
9768
20.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2768
16.7%
8768
16.7%
.768
16.7%
4768
16.7%
0768
16.7%
9768
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2768
16.7%
8768
16.7%
.768
16.7%
4768
16.7%
0768
16.7%
9768
16.7%

climate_p95_threshold
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
29.704
768 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4608
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29.704
2nd row29.704
3rd row29.704
4th row29.704
5th row29.704

Common Values

ValueCountFrequency (%)
29.704768
100.0%

Length

2025-11-25T00:06:08.243421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:08.277738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
29.704768
100.0%

Most occurring characters

ValueCountFrequency (%)
2768
16.7%
9768
16.7%
.768
16.7%
7768
16.7%
0768
16.7%
4768
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3840
83.3%
Other Punctuation768
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2768
20.0%
9768
20.0%
7768
20.0%
0768
20.0%
4768
20.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2768
16.7%
9768
16.7%
.768
16.7%
7768
16.7%
0768
16.7%
4768
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2768
16.7%
9768
16.7%
.768
16.7%
7768
16.7%
0768
16.7%
4768
16.7%

climate_p99_threshold
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
31.797
768 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4608
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31.797
2nd row31.797
3rd row31.797
4th row31.797
5th row31.797

Common Values

ValueCountFrequency (%)
31.797768
100.0%

Length

2025-11-25T00:06:08.313469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:08.347234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
31.797768
100.0%

Most occurring characters

ValueCountFrequency (%)
71536
33.3%
3768
16.7%
1768
16.7%
.768
16.7%
9768
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3840
83.3%
Other Punctuation768
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
71536
40.0%
3768
20.0%
1768
20.0%
9768
20.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
71536
33.3%
3768
16.7%
1768
16.7%
.768
16.7%
9768
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71536
33.3%
3768
16.7%
1768
16.7%
.768
16.7%
9768
16.7%

climate_season
Categorical

High correlation 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
Autumn
445 
Spring
120 
Winter
104 
Summer
99 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4608
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSummer
2nd rowAutumn
3rd rowSummer
4th rowAutumn
5th rowAutumn

Common Values

ValueCountFrequency (%)
Autumn445
57.9%
Spring120
 
15.6%
Winter104
 
13.5%
Summer99
 
12.9%

Length

2025-11-25T00:06:08.382537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:08.420040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
autumn445
57.9%
spring120
 
15.6%
winter104
 
13.5%
summer99
 
12.9%

Most occurring characters

ValueCountFrequency (%)
u989
21.5%
n669
14.5%
m643
14.0%
t549
11.9%
A445
9.7%
r323
 
7.0%
i224
 
4.9%
S219
 
4.8%
e203
 
4.4%
p120
 
2.6%
Other values (2)224
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3840
83.3%
Uppercase Letter768
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u989
25.8%
n669
17.4%
m643
16.7%
t549
14.3%
r323
 
8.4%
i224
 
5.8%
e203
 
5.3%
p120
 
3.1%
g120
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A445
57.9%
S219
28.5%
W104
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Latin4608
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u989
21.5%
n669
14.5%
m643
14.0%
t549
11.9%
A445
9.7%
r323
 
7.0%
i224
 
4.9%
S219
 
4.8%
e203
 
4.4%
p120
 
2.6%
Other values (2)224
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u989
21.5%
n669
14.5%
m643
14.0%
t549
11.9%
A445
9.7%
r323
 
7.0%
i224
 
4.9%
S219
 
4.8%
e203
 
4.4%
p120
 
2.6%
Other values (2)224
 
4.9%

fasting_glucose_mmol_L
Real number (ℝ)

Missing 

Fasting blood glucose

Distinct276
Distinct (%)37.5%
Missing32
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean4.928356
Minimum0.95
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:08.462437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile3.35
Q14.5
median4.93
Q35.4125
95-th percentile6.12
Maximum15
Range14.05
Interquartile range (IQR)0.9125

Descriptive statistics

Standard deviation0.95305831
Coefficient of variation (CV)0.1933826
Kurtosis19.566982
Mean4.928356
Median Absolute Deviation (MAD)0.45
Skewness1.5235001
Sum3627.27
Variance0.90832015
MonotonicityNot monotonic
2025-11-25T00:06:08.510036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.219
 
1.2%
4.759
 
1.2%
5.428
 
1.0%
4.828
 
1.0%
4.938
 
1.0%
4.78
 
1.0%
5.248
 
1.0%
4.737
 
0.9%
4.577
 
0.9%
5.177
 
0.9%
Other values (266)657
85.5%
(Missing)32
 
4.2%
ValueCountFrequency (%)
0.951
0.1%
1.121
0.1%
1.371
0.1%
1.471
0.1%
2.021
0.1%
2.041
0.1%
2.211
0.1%
2.221
0.1%
2.261
0.1%
2.552
0.3%
ValueCountFrequency (%)
151
0.1%
9.911
0.1%
9.671
0.1%
8.241
0.1%
7.971
0.1%
7.621
0.1%
7.611
0.1%
7.421
0.1%
7.271
0.1%
7.061
0.1%

total_cholesterol_mg_dL
Real number (ℝ)

High correlation  Missing 

Total cholesterol

Distinct331
Distinct (%)46.7%
Missing59
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean4.1249083
Minimum1.12
Maximum10.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:08.558624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.12
5-th percentile2.538
Q13.39
median4
Q34.81
95-th percentile6.016
Maximum10.48
Range9.36
Interquartile range (IQR)1.42

Descriptive statistics

Standard deviation1.1613229
Coefficient of variation (CV)0.28153907
Kurtosis3.3551237
Mean4.1249083
Median Absolute Deviation (MAD)0.7
Skewness1.0133115
Sum2924.56
Variance1.3486708
MonotonicityNot monotonic
2025-11-25T00:06:08.696729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410
 
1.3%
4.119
 
1.2%
3.627
 
0.9%
4.576
 
0.8%
3.896
 
0.8%
3.486
 
0.8%
4.936
 
0.8%
3.686
 
0.8%
2.775
 
0.7%
3.525
 
0.7%
Other values (321)643
83.7%
(Missing)59
 
7.7%
ValueCountFrequency (%)
1.121
0.1%
1.221
0.1%
1.291
0.1%
1.381
0.1%
1.541
0.1%
1.591
0.1%
1.82
0.3%
1.851
0.1%
2.012
0.3%
2.061
0.1%
ValueCountFrequency (%)
10.481
0.1%
10.291
0.1%
9.282
0.3%
9.041
0.1%
8.651
0.1%
7.71
0.1%
7.591
0.1%
7.31
0.1%
7.281
0.1%
6.821
0.1%

hdl_cholesterol_mg_dL
Real number (ℝ)

High correlation  Missing 

HDL cholesterol

Distinct174
Distinct (%)24.5%
Missing58
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.1211127
Minimum0.28
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:08.742310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.51
Q10.83
median1.07
Q31.37
95-th percentile1.8855
Maximum3.7
Range3.42
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation0.44352229
Coefficient of variation (CV)0.39560902
Kurtosis4.4712255
Mean1.1211127
Median Absolute Deviation (MAD)0.26
Skewness1.2913394
Sum795.99
Variance0.19671202
MonotonicityNot monotonic
2025-11-25T00:06:08.790967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.813
 
1.7%
1.0413
 
1.7%
0.8513
 
1.7%
0.9313
 
1.7%
1.113
 
1.7%
1.1811
 
1.4%
0.9511
 
1.4%
110
 
1.3%
0.8410
 
1.3%
0.879
 
1.2%
Other values (164)594
77.3%
(Missing)58
 
7.6%
ValueCountFrequency (%)
0.281
0.1%
0.321
0.1%
0.332
0.3%
0.342
0.3%
0.351
0.1%
0.362
0.3%
0.372
0.3%
0.391
0.1%
0.42
0.3%
0.412
0.3%
ValueCountFrequency (%)
3.73
0.4%
2.81
 
0.1%
2.532
0.3%
2.491
 
0.1%
2.441
 
0.1%
2.311
 
0.1%
2.31
 
0.1%
2.291
 
0.1%
2.242
0.3%
2.231
 
0.1%

ldl_cholesterol_mg_dL
Real number (ℝ)

High correlation  Missing 

LDL cholesterol

Distinct261
Distinct (%)36.8%
Missing58
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.6717042
Minimum0
Maximum6.04
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:08.839971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6745
Q11.11
median1.535
Q32.07
95-th percentile3.18
Maximum6.04
Range6.04
Interquartile range (IQR)0.96

Descriptive statistics

Standard deviation0.77008108
Coefficient of variation (CV)0.4606563
Kurtosis1.8978142
Mean1.6717042
Median Absolute Deviation (MAD)0.475
Skewness1.0866871
Sum1186.91
Variance0.59302488
MonotonicityNot monotonic
2025-11-25T00:06:08.888116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.019
 
1.2%
1.129
 
1.2%
1.329
 
1.2%
1.378
 
1.0%
1.298
 
1.0%
1.187
 
0.9%
2.067
 
0.9%
1.947
 
0.9%
1.267
 
0.9%
1.767
 
0.9%
Other values (251)632
82.3%
(Missing)58
 
7.6%
ValueCountFrequency (%)
01
 
0.1%
0.331
 
0.1%
0.391
 
0.1%
0.422
 
0.3%
0.451
 
0.1%
0.461
 
0.1%
0.471
 
0.1%
0.53
0.4%
0.555
0.7%
0.564
0.5%
ValueCountFrequency (%)
6.041
0.1%
4.411
0.1%
4.281
0.1%
4.252
0.3%
4.191
0.1%
4.131
0.1%
3.971
0.1%
3.941
0.1%
3.891
0.1%
3.871
0.1%

weight_kg
Real number (ℝ)

High correlation  Missing 

Body weight in kilograms

Distinct360
Distinct (%)63.9%
Missing205
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean69.787744
Minimum35.1
Maximum140.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:08.936961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum35.1
5-th percentile47.61
Q157.9
median67.2
Q378.4
95-th percentile102.99
Maximum140.5
Range105.4
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation16.938157
Coefficient of variation (CV)0.24270962
Kurtosis1.3539238
Mean69.787744
Median Absolute Deviation (MAD)10
Skewness0.98611018
Sum39290.5
Variance286.90115
MonotonicityNot monotonic
2025-11-25T00:06:08.986856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.35
 
0.7%
65.45
 
0.7%
59.64
 
0.5%
544
 
0.5%
53.74
 
0.5%
76.64
 
0.5%
65.64
 
0.5%
61.84
 
0.5%
55.14
 
0.5%
69.44
 
0.5%
Other values (350)521
67.8%
(Missing)205
 
26.7%
ValueCountFrequency (%)
35.11
0.1%
35.81
0.1%
36.41
0.1%
39.81
0.1%
41.61
0.1%
41.81
0.1%
421
0.1%
42.12
0.3%
42.51
0.1%
43.61
0.1%
ValueCountFrequency (%)
140.51
0.1%
135.21
0.1%
133.81
0.1%
130.61
0.1%
129.11
0.1%
121.91
0.1%
1181
0.1%
116.31
0.1%
115.81
0.1%
114.71
0.1%

height_m
Real number (ℝ)

High correlation  Missing 

Height in meters

Distinct194
Distinct (%)44.4%
Missing331
Missing (%)43.1%
Infinite0
Infinite (%)0.0%
Mean1.5870046
Minimum1.39
Maximum1.785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2025-11-25T00:06:09.035275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.39
5-th percentile1.4918
Q11.552
median1.589
Q31.619
95-th percentile1.6772
Maximum1.785
Range0.395
Interquartile range (IQR)0.067

Descriptive statistics

Standard deviation0.057610126
Coefficient of variation (CV)0.036301172
Kurtosis1.2076171
Mean1.5870046
Median Absolute Deviation (MAD)0.034
Skewness-0.018390588
Sum693.521
Variance0.0033189266
MonotonicityNot monotonic
2025-11-25T00:06:09.089058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5847
 
0.9%
1.5917
 
0.9%
1.5887
 
0.9%
1.617
 
0.9%
1.6066
 
0.8%
1.5686
 
0.8%
1.66
 
0.8%
1.5956
 
0.8%
1.5986
 
0.8%
1.5855
 
0.7%
Other values (184)374
48.7%
(Missing)331
43.1%
ValueCountFrequency (%)
1.391
0.1%
1.4041
0.1%
1.4051
0.1%
1.4061
0.1%
1.4161
0.1%
1.4171
0.1%
1.4571
0.1%
1.461
0.1%
1.4661
0.1%
1.4671
0.1%
ValueCountFrequency (%)
1.7851
0.1%
1.781
0.1%
1.7621
0.1%
1.7591
0.1%
1.7571
0.1%
1.7331
0.1%
1.7171
0.1%
1.7151
0.1%
1.711
0.1%
1.7081
0.1%

sa_biomarker_standards
Categorical

Constant 

South African biomarker reference standards

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
1.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0768
100.0%

Length

2025-11-25T00:06:09.138719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:09.173440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1768
50.0%
0768
50.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

cd4_correction_applied
Categorical

Constant 

Quality flag: CD4 missing codes removed

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0768
100.0%

Length

2025-11-25T00:06:09.209557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:09.243242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

final_comprehensive_fix_applied
Categorical

Constant 

Quality flag: Comprehensive corrections applied

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
1.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0768
100.0%

Length

2025-11-25T00:06:09.279071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:09.314180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1768
50.0%
0768
50.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1768
33.3%
.768
33.3%
0768
33.3%

total_protein_extreme_flag
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0768
100.0%

Length

2025-11-25T00:06:09.349585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:09.385359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
66.7%
.768
33.3%
Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0768
100.0%

Length

2025-11-25T00:06:09.420912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:09.454361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
66.7%
.768
33.3%
Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0768
100.0%

Length

2025-11-25T00:06:09.492168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:09.526333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01536
100.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01536
66.7%
.768
33.3%

quality_harmonization_version
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
2.0
768 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0768
100.0%

Length

2025-11-25T00:06:09.562471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T00:06:09.596273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0768
100.0%

Most occurring characters

ValueCountFrequency (%)
2768
33.3%
.768
33.3%
0768
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1536
66.7%
Other Punctuation768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2768
50.0%
0768
50.0%
Other Punctuation
ValueCountFrequency (%)
.768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2768
33.3%
.768
33.3%
0768
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2768
33.3%
.768
33.3%
0768
33.3%

waist_circ_unit_correction_applied
Boolean

High correlation 

Quality flag: Waist circ unit corrected

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
True
563 
False
205 
ValueCountFrequency (%)
True563
73.3%
False205
 
26.7%
2025-11-25T00:06:09.626270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2025-11-25T00:06:04.043777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:47.466898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.435387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.237025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.011311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.853281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.581215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.306547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.065653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.916205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.685885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.436218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.270774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.024270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.863815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.633834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.442815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.294424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.035876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.750316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.499266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.327851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.076637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:47.522999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.469901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.269179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.043151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.885898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.612343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.339322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.100575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.949326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.718675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.555949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.304764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.056862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.895993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.669178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.474528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.327406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.066172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.782506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.532056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.360186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.111699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:47.567366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.505943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.306028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.078356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.918644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.647071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.375629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.136926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.984731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.753883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.589630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.338662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.092079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.930827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.707062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.508401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.362829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.100422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.818504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.567000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.392195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.145144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:47.630420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.542566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.343630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.114084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.950643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.679169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.410361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.257756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.020394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.787923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.623061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.373753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.125941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.964738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.742526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.542699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.396994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.133522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.853388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.692018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.425701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.178979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:47.678812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.579905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.377770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.149638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.984070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.712176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.444781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.289863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.055113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.821669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.655158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.406955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.160579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.999686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.779873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.574802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.431264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.167187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.889570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.726847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.456727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.209308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:47.746455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.612692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.410799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.180174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.014006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.742553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.476204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.324379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.087490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.853233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.686705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.437885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.191400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.030380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.811892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.605104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.461723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.197492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.920251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.759414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.485903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.241320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:47.779470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.648462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.441895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.213417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.044576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.770787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.507311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.355957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.118431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.883747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.717760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.470605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.223752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.060705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.844486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.637246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.493221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.227708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.951848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.790281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.517050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.276839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:47.813043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.685591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-11-25T00:05:52.110784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.858434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.715920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.476405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.232105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.068856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.817839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.572814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.413841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.233707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.985488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.834039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.554878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.297207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.126398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.849666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.620899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.180817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.053215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.838939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.591943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.419993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.143341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.892656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.749976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.511957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.266541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.103213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.853394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.607412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.449483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.269880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.020602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.868921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.587462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.332260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.159907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.881177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.652320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.302816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.089503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.870766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.712034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.450295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.173869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.924867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.782535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.545561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.299241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.134953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.885059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.639355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.482003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.304012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.050479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.900725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.617007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.365019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.193055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.911963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.687454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.337130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.124947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.906502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.747493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.483478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.207072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.960220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.816372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.580596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.334105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.169025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.921515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.673355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.519384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.339287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.084763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.935210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.651781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.399319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.227876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.945076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.719074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.371403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.162015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.942213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.784161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.518063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.239972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.995914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.849672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.616268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.368825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.202317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.955193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.707001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.555062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.374933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.119394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.970337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.685246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.433899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.261437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.978036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.752379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:48.401686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.198896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:49.974852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:50.818260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:51.547165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:52.272241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.028581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:53.881176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:54.649143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:55.401781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.235764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:56.988630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:57.739699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:58.592068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:05:59.406515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:00.153836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.000253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:01.715543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:02.465913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:03.294368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T00:06:04.008062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T00:06:09.665717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Age (at enrolment)BMI (kg/m²)FASTING HDLFASTING LDLWaist circumference (cm)climate_14d_mean_tempclimate_30d_mean_tempclimate_7d_max_tempclimate_7d_mean_tempclimate_daily_max_tempclimate_daily_mean_tempclimate_daily_min_tempclimate_heat_day_p90climate_heat_stress_indexclimate_seasonclimate_standardized_anomalyclimate_temp_anomalyfasting_glucose_mmol_Lhdl_cholesterol_mg_dLheight_mldl_cholesterol_mg_dLmonthseasontotal_cholesterol_mg_dLwaist_circ_unit_correction_appliedweight_kgyear
Age (at enrolment)1.0000.2290.0120.1570.285-0.038-0.0240.031-0.0320.0510.017-0.0250.0730.0110.0140.0920.0720.1540.012-0.0370.1570.0080.0140.1550.0000.2180.091
BMI (kg/m²)0.2291.0000.0200.1060.8950.0280.0310.0520.0440.0360.0400.0300.0000.0410.0000.0200.0070.1160.020-0.1120.106-0.0270.0000.0960.0000.9370.000
FASTING HDL0.0120.0201.0000.2690.0040.1880.2040.1750.187-0.0980.0790.1891.000-0.1130.150-0.168-0.1690.0081.000-0.0210.269-0.1020.1500.5070.1020.0210.159
FASTING LDL0.1570.1060.2691.0000.0910.1410.0960.1140.2050.0540.1650.1721.0000.0950.092-0.025-0.0150.0320.269-0.0811.000-0.2100.0920.5660.0940.0930.107
Waist circumference (cm)0.2850.8950.0040.0911.0000.0550.0730.0580.0470.0250.0600.0591.0000.0310.0440.038-0.0330.1740.0040.0470.091-0.0420.0440.1111.0000.8960.016
climate_14d_mean_temp-0.0380.0280.1880.1410.0551.0000.9770.4830.9480.1520.7970.9000.0000.2270.768-0.416-0.568-0.0070.188-0.0690.141-0.4940.7680.1380.6380.0330.569
climate_30d_mean_temp-0.0240.0310.2040.0960.0730.9771.0000.5110.9110.0940.7390.8790.0000.1460.656-0.472-0.623-0.0030.204-0.0720.096-0.4450.6560.1550.5630.0440.484
climate_7d_max_temp0.0310.0520.1750.1140.0580.4830.5111.0000.5440.4290.6230.2710.6990.4870.634-0.0930.094-0.0930.175-0.0920.114-0.0460.6340.1630.5910.0440.611
climate_7d_mean_temp-0.0320.0440.1870.2050.0470.9480.9110.5441.0000.1540.7940.8810.0000.2650.868-0.394-0.497-0.0060.187-0.0800.205-0.6040.8680.1240.7160.0320.554
climate_daily_max_temp0.0510.036-0.0980.0540.0250.1520.0940.4290.1541.0000.631-0.0840.9960.7620.6320.5170.649-0.049-0.098-0.0870.0540.2910.632-0.0320.6600.0470.541
climate_daily_mean_temp0.0170.0400.0790.1650.0600.7970.7390.6230.7940.6311.0000.6630.0850.6290.7300.093-0.023-0.0080.079-0.0690.165-0.3210.7300.0820.4850.0460.753
climate_daily_min_temp-0.0250.0300.1890.1720.0590.9000.8790.2710.881-0.0840.6631.0000.0910.0330.678-0.396-0.7030.0700.189-0.0450.172-0.7380.6780.1010.6290.0370.658
climate_heat_day_p900.0730.0001.0001.0001.0000.0000.0000.6990.0000.9960.0850.0911.0000.3410.0700.0670.9950.0001.0001.0001.0000.1250.0701.0000.0001.0000.000
climate_heat_stress_index0.0110.041-0.1130.0950.0310.2270.1460.4870.2650.7620.6290.0330.3411.0000.5590.3070.438-0.132-0.113-0.1100.0950.0280.559-0.0080.5850.0320.473
climate_season0.0140.0000.1500.0920.0440.7680.6560.6340.8680.6320.7300.6780.0700.5591.0000.6430.7540.1850.1500.0620.0920.9931.0000.1430.9080.1010.416
climate_standardized_anomaly0.0920.020-0.168-0.0250.038-0.416-0.472-0.093-0.3940.5170.093-0.3960.0670.3070.6431.0000.6840.206-0.1680.075-0.0250.2340.643-0.1260.5830.0500.707
climate_temp_anomaly0.0720.007-0.169-0.015-0.033-0.568-0.6230.094-0.4970.649-0.023-0.7030.9950.4380.7540.6841.000-0.050-0.1690.020-0.0150.5720.754-0.0930.734-0.0010.773
fasting_glucose_mmol_L0.1540.1160.0080.0320.174-0.007-0.003-0.093-0.006-0.049-0.0080.0700.000-0.1320.1850.206-0.0501.0000.008-0.0550.032-0.1600.185-0.0660.2270.1330.186
hdl_cholesterol_mg_dL0.0120.0201.0000.2690.0040.1880.2040.1750.187-0.0980.0790.1891.000-0.1130.150-0.168-0.1690.0081.000-0.0210.269-0.1020.1500.5070.1020.0210.159
height_m-0.037-0.112-0.021-0.0810.047-0.069-0.072-0.092-0.080-0.087-0.069-0.0451.000-0.1100.0620.0750.020-0.055-0.0211.000-0.0810.0820.062-0.0391.0000.1850.000
ldl_cholesterol_mg_dL0.1570.1060.2691.0000.0910.1410.0960.1140.2050.0540.1650.1721.0000.0950.092-0.025-0.0150.0320.269-0.0811.000-0.2100.0920.5660.0940.0930.107
month0.008-0.027-0.102-0.210-0.042-0.494-0.445-0.046-0.6040.291-0.321-0.7380.1250.0280.9930.2340.572-0.160-0.1020.082-0.2101.0000.9930.0120.976-0.0230.483
season0.0140.0000.1500.0920.0440.7680.6560.6340.8680.6320.7300.6780.0700.5591.0000.6430.7540.1850.1500.0620.0920.9931.0000.1430.9080.1010.416
total_cholesterol_mg_dL0.1550.0960.5070.5660.1110.1380.1550.1630.124-0.0320.0820.1011.000-0.0080.143-0.126-0.093-0.0660.507-0.0390.5660.0120.1431.0000.1250.0840.098
waist_circ_unit_correction_applied0.0000.0000.1020.0941.0000.6380.5630.5910.7160.6600.4850.6290.0000.5850.9080.5830.7340.2270.1021.0000.0940.9760.9080.1251.0001.0000.475
weight_kg0.2180.9370.0210.0930.8960.0330.0440.0440.0320.0470.0460.0371.0000.0320.1010.050-0.0010.1330.0210.1850.093-0.0230.1010.0841.0001.0000.000
year0.0910.0000.1590.1070.0160.5690.4840.6110.5540.5410.7530.6580.0000.4730.4160.7070.7730.1860.1590.0000.1070.4830.4160.0980.4750.0001.000

Missing values

2025-11-25T00:06:04.918302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T00:06:05.042031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T00:06:05.171385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

study_sourceprimary_dateyearmonthseasonlatitudelongitudejhb_subregioncityprovincecountryAge (at enrolment)dateFASTING HDLFASTING LDLcoordinate_sourcecoordinate_precisiongeographic_sourceHEAT_VULNERABILITY_SCOREHEAT_STRESS_RISK_CATEGORYjohannesburg_metro_validstudy_site_locationBMI (kg/m²)Waist circumference (cm)climate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_7d_mean_tempclimate_7d_max_tempclimate_14d_mean_tempclimate_30d_mean_tempclimate_temp_anomalyclimate_standardized_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_seasonfasting_glucose_mmol_Ltotal_cholesterol_mg_dLhdl_cholesterol_mg_dLldl_cholesterol_mg_dLweight_kgheight_msa_biomarker_standardscd4_correction_appliedfinal_comprehensive_fix_appliedtotal_protein_extreme_flagdphru_053_final_corrections_appliedezin_002_final_corrections_appliedquality_harmonization_versionwaist_circ_unit_correction_applied
217JHB_DPHRU_0132011-02-102011.02.0Summer-26.204128.0473Central_JHBJohannesburgGautengSouth Africa19.42011-02-101.231.41JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)24.283.019.59925.32514.05720.25327.10519.47518.9666.360-0.1370.00.020.17528.40929.70431.797Summer5.032.771.231.4159.81.5841.00.01.00.00.00.02.0True
218JHB_DPHRU_0132011-04-092011.04.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa39.42011-04-090.901.54JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)33.6103.014.60320.5899.00415.06421.26415.48315.7344.855-0.4490.00.016.76528.40929.70431.797Autumn4.554.930.901.5483.91.5891.00.01.00.00.00.02.0True
219JHB_DPHRU_0132012-01-212012.01.0Summer-26.204128.0473Central_JHBJohannesburgGautengSouth AfricaNaN2012-01-211.332.20JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)33.1NaN20.46525.57213.45419.63126.04519.33420.1755.3970.2170.00.022.86728.40929.70431.797Summer4.765.111.332.20NaNNaN1.00.01.00.00.00.02.0False
220JHB_DPHRU_0132012-04-022012.04.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa40.02012-04-021.612.37JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)33.5102.014.68518.89611.18715.63323.49816.63716.7322.164-0.4100.00.014.37328.40929.70431.797Autumn6.725.351.612.3784.71.5981.00.01.00.00.00.02.0True
221JHB_DPHRU_0132013-05-162013.05.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa42.02013-05-161.713.36JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)30.189.013.31620.7686.61612.66520.76812.57012.8567.9130.1900.00.015.72128.40929.70431.797Autumn5.685.891.713.3676.0NaN1.00.01.00.00.00.02.0True
222JHB_DPHRU_0132011-03-192011.03.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa39.02011-03-191.163.03JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)22.077.017.03920.32414.29118.08123.49619.00919.1391.185-0.7020.00.016.47628.40929.70431.797Autumn5.033.971.163.0368.01.7621.00.01.00.00.00.02.0True
223JHB_DPHRU_0132011-08-272011.08.0Winter-26.204128.0473Central_JHBJohannesburgGautengSouth Africa40.02011-08-270.522.48JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)21.5NaN16.42525.9316.30614.19826.29214.80013.94711.9841.0900.00.021.05728.40929.70431.797Winter4.322.520.522.48NaNNaN1.00.01.00.00.00.02.0False
224JHB_DPHRU_0132012-02-092012.02.0Summer-26.204128.0473Central_JHBJohannesburgGautengSouth Africa40.02012-02-090.952.71JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)21.277.020.35724.31917.50719.98225.89320.67920.6003.7190.2980.00.020.51828.40929.70431.797Summer5.484.170.952.7165.01.7591.00.01.00.00.00.02.0True
225JHB_DPHRU_0132013-05-092013.05.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa41.02013-05-091.042.60JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)21.677.013.31620.7686.61612.66520.76812.57012.8567.9130.1900.00.015.72128.40929.70431.797Autumn5.264.471.042.6066.1NaN1.00.01.00.00.00.02.0True
226JHB_DPHRU_0132011-03-172011.03.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa22.22011-03-170.902.17JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)19.369.017.03920.32414.29118.08123.49619.00919.1391.185-0.7020.00.016.47628.40929.70431.797Autumn4.253.130.902.1751.81.6461.00.01.00.00.00.02.0True
study_sourceprimary_dateyearmonthseasonlatitudelongitudejhb_subregioncityprovincecountryAge (at enrolment)dateFASTING HDLFASTING LDLcoordinate_sourcecoordinate_precisiongeographic_sourceHEAT_VULNERABILITY_SCOREHEAT_STRESS_RISK_CATEGORYjohannesburg_metro_validstudy_site_locationBMI (kg/m²)Waist circumference (cm)climate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_7d_mean_tempclimate_7d_max_tempclimate_14d_mean_tempclimate_30d_mean_tempclimate_temp_anomalyclimate_standardized_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_seasonfasting_glucose_mmol_Ltotal_cholesterol_mg_dLhdl_cholesterol_mg_dLldl_cholesterol_mg_dLweight_kgheight_msa_biomarker_standardscd4_correction_appliedfinal_comprehensive_fix_appliedtotal_protein_extreme_flagdphru_053_final_corrections_appliedezin_002_final_corrections_appliedquality_harmonization_versionwaist_circ_unit_correction_applied
975JHB_DPHRU_0132011-06-112011.06.0Winter-26.204128.0473Central_JHBJohannesburgGautengSouth Africa25.92011-06-111.141.57JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)21.568.09.61617.3443.3338.99318.6998.8269.5847.760-0.1930.00.013.35128.40929.70431.797Winter4.764.961.141.5750.41.5381.00.01.00.00.00.02.0True
976JHB_DPHRU_0132012-01-212012.01.0Summer-26.204128.0473Central_JHBJohannesburgGautengSouth AfricaNaN2012-01-211.322.19JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)22.6NaN20.46525.57213.45419.63126.04519.33420.1755.3970.2170.00.022.86728.40929.70431.797Summer4.976.241.322.19NaNNaN1.00.01.00.00.00.02.0False
977JHB_DPHRU_0132012-05-122012.05.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa27.02012-05-121.962.68JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)23.672.014.55020.1088.03515.08023.70515.22615.2084.9000.7160.00.016.44228.40929.70431.797AutumnNaN6.821.962.6854.61.5251.00.01.00.00.00.02.0True
978JHB_DPHRU_0132011-06-112011.06.0Winter-26.204128.0473Central_JHBJohannesburgGautengSouth Africa33.32011-06-111.101.05JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)32.497.09.61617.3443.3338.99318.6998.8269.5847.760-0.1930.00.013.35128.40929.70431.797Winter5.383.701.101.0577.81.5541.00.01.00.00.00.02.0True
979JHB_DPHRU_0132011-11-162011.011.0Spring-26.204128.0473Central_JHBJohannesburgGautengSouth Africa34.02011-11-161.401.72JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)34.4NaN19.08426.13612.46517.69526.20419.62920.9485.189-0.0690.00.015.36028.40929.70431.797Spring5.505.321.401.72NaNNaN1.00.01.00.00.00.02.0False
980JHB_DPHRU_0132012-05-022012.05.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa34.02012-05-021.761.32JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)37.3115.514.55020.1088.03515.08023.70515.22615.2084.9000.7160.00.016.44228.40929.70431.797Autumn5.994.111.761.3290.81.5621.00.01.00.00.00.02.0True
981JHB_DPHRU_0132013-05-082013.05.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa35.02013-05-080.421.35JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)37.9103.013.31620.7686.61612.66520.76812.57012.8567.9130.1900.00.015.72128.40929.70431.797Autumn6.112.350.421.3591.1NaN1.00.01.00.00.00.02.0True
982JHB_DPHRU_0132011-06-072011.06.0Winter-26.204128.0473Central_JHBJohannesburgGautengSouth Africa31.32011-06-070.911.00JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)31.8101.09.61617.3443.3338.99318.6998.8269.5847.760-0.1930.00.013.35128.40929.70431.797Winter5.213.520.911.0084.61.6301.00.01.00.00.00.02.0True
983JHB_DPHRU_0132011-11-102011.011.0Spring-26.204128.0473Central_JHBJohannesburgGautengSouth Africa32.02011-11-101.020.59JHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)31.2NaN19.08426.13612.46517.69526.20419.62920.9485.189-0.0690.00.015.36028.40929.70431.797Spring4.672.931.020.59NaNNaN1.00.01.00.00.00.02.0False
984JHB_DPHRU_0132012-05-022012.05.0Autumn-26.204128.0473Central_JHBJohannesburgGautengSouth Africa32.02012-05-02NaNNaNJHB_DPHRU_013highharmonized_datasets0.0LOW1.0Central Johannesburg (DPHRU)33.2104.014.55020.1088.03515.08023.70515.22615.2084.9000.7160.00.016.44228.40929.70431.797Autumn5.76NaNNaNNaN87.21.6271.00.01.00.00.00.02.0True